2019年高级计量经济学考试

高级计量经济学考试一、单选题（25*2分）Whichofthefollowingcorrectlyidentifiesadifferencebetweencross-sectionaldataandtimeseriesdata?Cross-sectionaldataisbasedontemporalordering,whereastimeseriesdataisnot.Timeseriesdataisbasedontemporalordering,whereascrosssectionaldataisnot.Cross-sectionaldataconsistsofonlyqualitativevariables,whereastimeseriesdataconsistsofonlyquantitativevariables.Timeseriesdataconsistsofonlyqualitativevariables,whereascross-sectionaldatadoesnotincludequalitativevariables.Astochasticprocessreferstoa:sequenceofrandomvariablesindexedbytime.sequenceofvariablesthatcantakefixedqualitativevalues.sequenceofrandomvariablesthatcantakebinaryvaluesonly.sequenceofrandomvariablesestimatedatthesamepointoftime.Themodel: = 0+ 1 + ,t=1,2,…….,nisanexampleofa(n):Autoregressiveconditionalheteroskedasticitymodel.staticmodel.finitedistributedlagmodel.infinitedistributedlagmodel.Refertothefollowingmodel = 0+ 0 + 1 −1+ 2 −2+ 3 −3+ Thisisanexampleofa(n):infinitedistributedlagmodel.finitedistributedlagmodeloforder1.finitedistributedlagmodeloforder2.finitedistributedlagmodeloforder3.5.Refertothefollowingmodel. = 0+ 0 + 1 −1+ 2 −2+ 3 −3+ 0+ 1+ 2+ represents:theshort-runchangeinygivenatemporaryincreaseins.theshort-runchangeinygivenapermanentincreaseins.thelong-runchangeinygivenapermanentincreaseins.thelong-runchangeinygivenatemporaryincreaseins.Whichofthefollowingisanassumptiononwhichtimeseriesregressionisbased?Atimeseriesprocessfollowsamodelthatisnonlinearinparameters.Inatimeseriesprocess,noindependentvariableisaperfectlinearcombinationoftheothers.Inatimeseriesprocess,atleastoneindependentvariableisaconstant.Foreachtimeperiod,theexpectedvalueoftheerrorut,giventheexplanatoryvariablesforalltimeperiods,ispositive.Aseasonallyadjustedseriesisonewhich:hashadseasonalfactorsaddedtoit.hasseasonalfactorsremovedfromit.hasqualitativedependentvariablesrepresentingdifferentseasons.hasqualitativeexplanatoryvariablesrepresentingdifferentseasons.Aprocessisstationaryif:anycollectionofrandomvariablesinasequenceistakenandshiftedaheadbyhtimeperiods;thejointprobabilitydistributionchanges.anycollectionofrandomvariablesinasequenceistakenandshiftedaheadbyhtimeperiods,thejointprobabilitydistributionremainsunchanged.thereisserialcorrelationbetweentheerrortermsofsuccessivetimeperiodsandtheexplanatoryvariablesandtheerrortermshavepositivecovariance.thereisnoserialcorrelationbetweentheerrortermsofsuccessivetimeperiodsandtheexplanatoryvariablesandtheerrortermshavepositivecovariance.Astochasticprocess{ :t=1,2,….}withafinitesecondmoment[E( 2)< ∞]iscovariancestationaryif:E( )isvariable,Var( )isvariable,andforanyt,h ≥1,Cov( , +ℎ)dependsonlyon‘h’andnotonE( )isvariable,Var( )isvariable,andforanyt,h≥1,Cov( , +ℎ)dependsonlyon‘t’andnotonh.E( )isconstant,Var( )isconstant,andforanyt,h≥1,Cov( , +ℎ)dependsonlyon‘h’andnotonE( )isconstant,Var( )isconstant,andforanyt,h ≥1,Cov( , +ℎ)dependsonlyon‘t’andnoton‘h’.Acovariancestationarytimeseriesisweaklydependentif:thecorrelationbetweentheindependentvariableattime ‘t’andthedependentvariableattime‘t+h’goesto∞ash→0.thecorrelationbetweentheindependentvariableattime ‘t’andthedependentvariableattime‘t+h’goesto0ash→∞.thecorrelationbetweentheindependentvariableattime‘t’andtheindependentvariableattime‘t+h’goesto0ash→∞.thecorrelationbetweentheindependentvariableattime‘t’andtheindependentvariableattime‘t+h’goesto∞ash→∞.Themodel = 1 −1+ ,t=1,2,….,where isani.i.d.sequencewithzeromeanandvariance 2representsa(n):movingaverageprocessoforderone.movingaverageprocessofordertwo.autoregressiveprocessoforderone.autoregressiveprocessofordertwo.Whichofthefollowingisassumedintimeseriesregression?Thereisnoperfectcollinearitybetweentheexplanatoryvariables.Theexplanatoryvariablesarecontemporaneouslyendogenous.Theerrortermsarecontemporaneouslyheteroskedastic.Theexplanatoryvariablescannothavetemporalordering.Considerthemodel: = 0+ 11 + 22 + .Underweakdependence,theconditionsufficientforconsistencyofOLSis:a.E(zt1|zt2)=0.E(yt|zt1,zt2)=0.E(ut|zt1,zt2)=0.E(ut|zt1,zt2)=∞.Themodel = −1+et,t=1,2,…representsa:AR(2)process.MA(1)process.randomwalkprocess.randomwalkwithadriftprocess.Whichofthefollowingstatementsistrue?Arandomwalkprocessisstationary.Thevarianceofarandomwalkprocessincreasesasalinearfunctionoftime.Addingadrifttermtoarandomwalkprocessmakesitstationary.Thevarianceofarandomwalkprocesswithadriftdecreasesasanexponentialfunctionoftime.Ifaprocessissaidtobeintegratedoforderone,orI(1), .itisstationaryatlevelaveragesofsuchprocessesalreadysatisfythestandardlimittheoremsthefirstdifferenceoftheprocessisweaklydependentitdoesnothaveaunitrootInthepresenceofserialcorrelation:estimatedstandarderrorsremainvalid.estimatedteststatisticsremainvalid.estimatedOLSvaluesarenotBLUE.estimatedvariancedoesnotdifferfromthecaseofnoserialcorrelation.Whenaseriesisstationary,weaklydependent,andhasserialcorrelation:a.theadjusted 2isinconsistent,while 2isaconsistentestimatorofthepopulationparameter.b.theadjusted 2isconsistent,while 2isaninconsistentestimatorofthepopulationparameter.c.boththeadjusted 2and 2areinconsistentestimatorsofthepopulationparameter.d.boththeadjusted 2and 2areconsistentestimatorsofthepopulationparameter.Asmallerstandarderrormeans:alargertstatistic.asmallertstatistic.alargerFstatistic.asmallerFstatistic.Inamodelbasedonaweaklydependenttimeserieswithserialcorrelationandstrictlyexogenousexplanatoryvariables, .thefeasiblegeneralizedleastsquareestimatesareunbiasedthefeasiblegeneralizedleastsquareestimatesareBLUEthefeasiblegeneralizedleastsquareestimatesareasymptoticallymoreefficientthanOLSestimatesthefeasiblegeneralizedleastsquareestimatesareasymptoticallylessefficientthanOLSestimatesWhichofthefollowingidentifiesanadvantageoffirstdifferencingatime-series?Firstdifferencingeliminatesmostoftheserialcorrelation.Firstdifferencingeliminatesmostoftheheteroskedastcicty.Firstdifferencingeliminatesmostofthemulticollinearity.Firstdifferencingeliminatesthepossibilityofspuriousregression.Whichofthefollowingisalimitationofserialcorrelationrobuststandarderrors?Theserialcorrelation-robuststandarderrorsaresmallerthanOLSstandarderrorswhenthereisserialcorrelation.Theserialcorrelation-robuststandarderrorscanbepoorlybehavedwhenthereissubstantialserialcorrelationandthesamplesizeissmall.Theserialcorrelation-robuststandarderrorscannotbecalculatedforautoregressiveprocessesofanordergreaterthanone.Theserialcorrelation-robuststandarderrorscannotbecalculatedafterrelaxingtheassumptionofhomoskedasticity.nees,elntdsareshomoskedasticityandautocorrelationinconsistentstandarderrors.homoskedasticityandautocorrelationconsistentstandarderrors.heteroskedasticityand